Cluster Analysis: An Application to a Real Mixed-Type Data Set
When you dispose of multivariate data it is crucial to summarize them, so as to extract appropriate and useful information, and consequently, to make proper decisions accordingly. Cluster analysis fully meets this requirement; it groups data into meaningful groups such that both the similarity within a cluster and the dissimilarity between groups are maximized. Thanks to its great usefulness, clustering is used in a broad variety of contexts; this explains its huge appeal in many disciplines. Most of the existing clustering approaches are limited to numerical or categorical data only. However, since data sets composed of mixed types of attributes are very common in real life applications, it is absolutely worth to perform clustering on them. In this paper therefore we stress the importance of this approach, by implementing an application on a real world mixed-type data set.
KeywordsClusters analysis Numeric data Categorical data Mixed data Cluster algorithm
- Caruso, G., Gattone, S.A., Fortuna, F., Di Battista, T.: Cluster analysis as a decision-making tool: a methodological review. In: Bucciarelli, E., Chen, S., Corchado, J.M., (eds.) Decision Economics: In the Tradition of Herbert A. Simon’s Heritage. Advances in Intelligent Systems and Computing, vol. 618, pp. 48–55. Springer International Publishing (2018)Google Scholar
- Everitt, B.: Cluster Analysis. Heinemann Educational Books Ltd. (1974)Google Scholar
- Huang, Z.: Clustering large data sets with mixed numeric and categorical values. In: Proceedings in the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 21–34 (1997)Google Scholar
- MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
- Peng, Y., Kou, G., Shi. Y., Chen, Z.: Improving clustering analysis for credit card accounts classification. In: Proceedings of the 5th International Conference on Computational Science—ICCS 2005, Part III, pp. 548–553. Springer Berlin Heidelberg (2005)Google Scholar